The term MLOps was first coined in a 2015 research paper called, “hidden technical debt in machine learning system”. This paper outlined the challenges of dealing with large volume of data sets and how using Devops will help in managing ML practice better.
MLOps stands for machine learning operations. It is a method that controls the lifecycle of a machine learning model. It combines both machine learning and DevOps (a combination of software and operations to maintain the lifecycle of software).
For large-scale productions, MLOps not just help streamlining but also help in the deployment of ML models. Moreover, integrating MLOps will help reduce constant friction between data scientists and the tech operators, as the models can automate the routine tasks by themselves.
The growing use of MLOps in increasing and it is likely to grow more. A study by Market.us, projects that a significant projection valuation of USD 75.42 billion would be witnessed by 2033, marking a remarkable CAGR jump of 43.2% from 2024 to 2033.
Source: market.us
Through this blog, we will explore and understand what is MLOps, its components, role of MLOps in supply chain and how is it better than ML through a case study. Let’s dive deep!
Key components of MLOps
MLOps, is a major part of machine learning engineering. It focuses on three major areas of production, maintenance and monitoring of the models.
Source:Medium
- Model Development – This phase includes developing model, training and validating it. The whole experiment is tracked using some of the tools such as, MLflow, Tensor Board, and Weights & Biases. For the proper validation data is managed by verifying the data quality, data versioning (keeping a history of changes in the data)., etc.
- Model Training – This phase involves providing the ML algorithms the data(information, numbers, text etc) so that the model gets trained and perform tasks based on its learning.
- Model Validation – Once, the model is trained, it needs to be validated with proper validation metrics. This includes, model accuracy, F1 score, precision etc. Cross validation is another technique used in measuring the robustness of the model.
- Model Deployment: This phase includes where we place the trained model in the real production environment, which helps to provide real-time predictions.
- Model Management: To make sure the model is performing as expected, it needs to be monitored constantly. Updating the model at times is important when the performance drops down.
- Model retirement: This is the last stage, where the model becomes obsolete, and it needs to be replaced to maintain the existing performance.
MLOps in Supply chain management
Integrating MLOps in supply chain management is a key element in the whole management process. MLOps helps in constant monitoring, which will help supply chain businesses to keep an eye on market fluctuation and product demands. In this competitive market space, which is continuously evolving, MLOps will help businesses cope with demand forecasting and inventory management. Some of the benefits of MLOps in SCM
- Quick adjustments – As MLOps utilize real-time analytics, it becomes easier to track the immediate shifts in market conditions, fluctuations in order demands etc.
- Easy view of complex supply chain - To track the supply chain management process, the data integration information is collected through various means, which sometimes makes it difficult to understand. With MLOps it all be brought into one comprehensive view.
- Maintaining accuracy and reliability – Constant monitoring and updating of data helps in maintaining the model accuracy and adaptability. By doing this, even after a period when we update models with new data, businesses can mitigate the risk of decay because of the continuous data changing patterns.
Applications of MLOps in Supply chain management
MLOps are becoming extremely crucial for the supply chain management due to its ability for providing more reliable, actionable and real time insights. Let’s look at some of the areas where MLOps is benefitted most in SCM.
- Inventory optimization – MLOps helps in adjusting and monitoring the inventory levels. This starts with collecting past sales data, cleaning the data, developing predictive models and so on. MLOps can be utilized for predicting the inventory accuracy by integrating predictive analytics. This would help to manage irregular stock issues, like overstocking and understocking.
- Sales prediction – Since MLOps are known for maintaining the lifecycle of ML models, it helps with sales predictions based on seasonality. Data are collected based on the sales trends, market conditions etc. By using these variables ML models can be built for accurate sales predictions.
- Accurate forecasting – With the help of advanced analytics and continuous improvement in the ML models, businesses can achieve accurate predictions in longer run.
- Logistics optimization – MLOps can help logistics department in optimizing right delivery routes. This can analyze the routes by looking into data such as traffic, weather and delivery schedules. By collecting some of these data and building models that can provide real-time tracking on these variables, can help with timely delivery of products.
Case Study
A Fortune 500 company, a major player in transportation, supply chain management, and logistics, which operates 700 container vessels across 130 countries faced challenges in the third-party logistics department (outsourcing to an external provider). The challenges faced includes warehouse restrictions, unexpected spikes in demand, increasing costs, talent shortages, etc.
With the motive of staying ahead in the competition, the company invested in new technology, especially ML models. This led the company to build different ML models catering to the specific needs of each department. However, this led to a proliferation of 1300 ML models built on various platforms, making it difficult for the company to handle the expenses.
To solve this problem, the company partnered with Prescience Decision Solutions to create an enterprise-level MLOps (machine learning operations) platform. The team assessed the existing ML models and set up new principles for building the MLOps platform, such as simplicity, scalability, security, & reliability.
The team builds an MLOps platform using tools like Redis, PostgreSQL, Kubeflow, Git, MLFlow and Azure Kubernetes Services. They successfully migrated 4 existing ML models to the new MLOps platform. This migration helped in auto-scaling, improved efficiency and overall cost reduction for the company. The major change that happened was the centralized model tracking, automated monitoring and maintaining model accuracy.
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Conclusion
MLOps play a crucial role in SCM, by managing inventory, sales predictions, real-time data tracking etc. This helps in bringing customer satisfaction, efficiency in internal operations, personalized recommendation for customers and so on. One of the key factors that stands out in MLOps is the constant automation of the ML process, i.e. from data collection to deploying a model, that too with accuracy.
With the technological advancement happening daily, adopting MLOps provides continuous improvement and agility in overall business operations, paving the way for success.
Prescience Team
